Why Use DDI?


DDI encourages comprehensive description of data for discovery and analysis and supports effective data sharing. Because DDI is a structured standard, it facilitates machine-actionability and interoperability and it can actually be used to drive systems. Another feature of DDI is its focus on metadata reuse; “enter once, use often” means you can reuse metadata over the course of the data life cycle to avoid costly duplication of effort.

DDI has advantages for several different audiences:

  • Standards are important to the effective functioning of libraries. Using a standard vocabulary to document research data leads to consistency and improved interoperability.
  • DDI is designed to make research data independently understandable.  DDI provides a standard structure for all of the metadata that accompanies a dataset and helps users of that dataset to interpret its contents. This is useful when assisting patrons and data analysts.
  • DDI is an open, non-proprietary standard and anyone can use it.
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  • Metadata are expensive to produce, so reusing structured, standardized metadata makes good business sense.
  • DDI promotes interoperability and thus supports partnerships with others that involve data and metadata exchange.
  • DDI’s structure can enable effective search and discovery, subsetting, generation of syntax files, and flexibility in display, resulting in many efficiencies.
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  • Codebooks have long been used to interpret data files, but PDF and Word codebooks are not “intelligent.” In contrast, DDI codebooks are structured and can be interactive, enabling users to navigate through a collection.
  • DDI can serve as a foundation for data catalogs as it provides a standard structure for searching at both the study and variable levels to enable users to discover data of interest.
  • Using DDI throughout the archiving life cycle can streamline the repository’s workflow, leading to efficient ingest, management, and preservation of data.
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  • Recent open access mandates from funders require that data be shared in order to validate results and to encourage new discoveries. This means that data must be well-documented, which is DDI’s strength.
  • Complex, longitudinal data projects require additional levels of data management. DDI can support this and can enable creation of reports, displays, and tools that leverage the richness of the data. Some examples are question banks, concordances, and interactive codebooks.
  • The structure of DDI can support data comparison and harmonization.
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  • Using a structured standard optimizes machine-actionability and makes programming against the structure possible.
  • DDI can actually drive process, leading to greater efficiencies.
  • DDI can be used with relational databases to increase flexibility.
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DDI Data Lifecycle